An improved transfer learning algorithm for document categorization based on data sets reconstruction

Traditional machine learning and data mining algorithms usually assume that the training and test data have the same feature space and data distribution, but in the real application this assumption is often difficult to establish, and always lead the existing model to outdate. As a new learning mechanism, transfer learning can solve this problem effectively, in this paper, we will propose an improved transfer learning algorithm for document categorization based on data sets reconstruct, we also describe the main idea and the step of the algorithm, then use experiment to test the algorithm and compare it with other algorithms, the result of experiment proves the algorithm we proposed in this paper is better than the others in some extent.